Data augmentation for cnn-based people detection in aerial images

Hua Tsung Chen, Che Han Liu, W. J. Tsai

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Much more than ever, many important places have deployed surveillance cameras for early detection of abnormal events and suspects. However, the monitoring ability of fixed cameras is significantly limited due to the low flexibility, blind spot, and obstacle occlusion. With high mobility, drones have high potential for supporting security surveillance. On the other hand, people detection plays a key role in intelligent surveillance system, and increasing deep learning-based methods show great results. However, the training data for aerial images are still few, even though there are many public datasets available. Thus, in this paper we research on data augmentation, try transforming general images to be aerial image-like, and make an attempt to improving the performance of deep learning-based people detection with existing datasets. The experiments conducted on the real aerial images collected by a camera drone show encouraging results.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538641958
DOIs
StatePublished - 28 Nov 2018
Event2018 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2018 - San Diego, United States
Duration: 23 Jul 201827 Jul 2018

Publication series

Name2018 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2018

Conference

Conference2018 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2018
CountryUnited States
CitySan Diego
Period23/07/1827/07/18

Keywords

  • aerial image
  • CNN
  • data augmentation
  • deep learning
  • Drone

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